An Efficient Model for Automatic Number Plate Detection using HOG Feature from New North Iraq Vehicle Images Dataset - Yaseen, Naaman Omar; Al-Ali, Salim Ganim Saeed; Sengur, Abdulkadir (Institute of Electrical and Electronics Engineers Inc., 2019)
the increasing number of vehicles in all over the world day by day, have made traffic control becomes a serious and major problem. Sometimes, it becomes difficult to detect the plate license number of a vehicle that violates traffic rules. In this paper, an efficient model is proposed to detect and locate number license plate of a vehicle that is given in color images. The model is constructed by using different features instead of single feature to improve its performance based on multi descriptors (texture features). The work use multi-boosting model (AdaBoost) based on HOG descriptors, which precisely localize the ROI of the given vehicle images. A new North Iraq Vehicle Images (NI-VI) dataset, which is used to test the proposed model, is introduced. The license number plates of North Iraq uses Arabic font. All images of this dataset, are taken under various circumstances and different weather conditions to simulate all realistic examples about vehicles. The developed model produced 89.66% accuracy score. © 2019 IEEE.
1st International Informatics and Software Engineering Conference, IISEC 2019 -- 6 November 2019 through 7 November 2019 -- Ankara -- 157111
Keywords: AdaBoost technique; Automatic Number Plate Detection (ANPD); Automatic Number Plate Recognition (ANPR); Cascade Detector Trainer; Vehicle datasets